For the three datasets we have in common with [1]} it is possible to draw performance comparisons with that work, though retaining our concerns about the use of AUROC for highly imbalanced data, which we found did not distinguish well between the methods. The two best-performing methods of [1]}, in relation to G-Mean and AUROC, were the HD-Ensemble method of [1]} and RUSBoost [4]}. For skin-588, GMN, BCE-ASTra, and GMN-ASTra all had statistically significantly indistinguishable performance from RUSBoost, with respect to both metrics. For cod-763, the two ASTra variants tied with RUSBoost on AUROC, but beat RUSBoost on G-Mean, while on ijcnn-971 they achieved a better G-Mean than state-of-the-art HD-Ensemble. Given especially that we used small, single-layer neural networks, with that used for skin-588 having only three neurons, we consider these results encouraging.
